snRNAseq analyses
Prerequisites
Packages required for this analyses are loaded as follows:
setwd("~/github/amnion.vs.other_RNASeq")
library(Seurat)
library(SeuratWrappers)
library(knitr)
library(kableExtra)
library(ggplot2)
library(cowplot)
library(patchwork)
library(metap)
library(multtest)
library(gridExtra)
library(dplyr)
library(stringr)
library(TissueEnrich)
library(gprofiler2)
library(tidyverse)
library(enhancedDimPlot)
library(calibrate)
library(ggrepel)
library(dittoSeq)
library(ComplexHeatmap)
library(scales)
library(plotly)Import datasets
The counts data and its associated metadata (coldata) are imported for analyses
experiment_name = "BAP"
dataset_loc <- "~/TutejaLab/expression-data"
ids <- c("5pcO2_r1", "5pcO2_r2", "20pcO2_r1", "20pcO2_r2", "nCT_D5", "nCT_D10", "nTE_D2", "nTE_D3")
d10x.data <- sapply(ids, function(i){
d10x <- Read10X(file.path(dataset_loc,i,"filtered_feature_bc_matrix"))
colnames(d10x) <- paste(sapply(strsplit(colnames(d10x),split="-"), '[[' , 1L ), i, sep="-")
d10x
})
experiment.data <- do.call("cbind", d10x.data)
bapd8.combined <- CreateSeuratObject(
experiment.data,
project = "BAPd8",
min.cells = 10,
min.genes = 200,
names.field = 2,
names.delim = "\\-")bapd8.temp <- bapd8.combined
bapd8.combined$log10GenesPerUMI <- log10(bapd8.combined$nFeature_RNA) / log10(bapd8.combined$nCount_RNA)
bapd8.combined$mitoRatio <- PercentageFeatureSet(object = bapd8.combined, pattern = "^MT-")
bapd8.combined$mitoRatio <- bapd8.combined@meta.data$mitoRatio / 100
metadata <- bapd8.combined@meta.data
metadata$cells <- rownames(metadata)
metadata <- metadata %>%
dplyr::rename(seq_folder = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA,
seq_folder = orig.ident)mt <- ggplot(dat = metadata, aes(x=nUMI, y=nGene, color=mitoRatio)) +
geom_point(alpha = 0.5) +
scale_colour_gradient(low = "gray90", high = "black") + labs(colour="MT ratio") +
theme_bw(base_size = 12) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
panel.border = element_rect(colour = "black")) +
xlab("RNA counts") + ylab("Gene counts") +
stat_smooth(method=lm) +
facet_wrap(~seq_folder, labeller = labeller(seq_folder =
c("20pcO2_r1" = "20% Oxygen (rep1)",
"20pcO2_r2" = "20% Oxygen (rep2)",
"5pcO2_r1" = "5% Oxygen (rep1)",
"5pcO2_r2" = "5% Oxygen (rep2)",
"nCT_D5" = "nCT day 5",
"nCT_D10" = "nCT day 10",
"nTE_D2" = "nTE day 2",
"nTE_D3" = "nTE day 3"))) +
scale_y_continuous(label=comma) +
scale_x_continuous(label=comma)
mt
#> `geom_smooth()` using formula 'y ~ x'
FigX: fig legend
ncells <- ggplot(metadata, aes(x=seq_folder, fill=seq_folder)) +
geom_bar() +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
theme(plot.title = element_text(hjust=0.5, face="bold")) +
ggtitle("Number of Cells")
ncells
FigX: fig legend
density of cells per sample
dcells <- ggplot(metadata, aes(color=seq_folder, x=nUMI, fill= seq_folder)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
ylab("Cell density") +
geom_vline(xintercept = 500)
dcells
FigX: fig legend
ngenes <- ggplot(metadata, aes(x=seq_folder, y=log10(nGene), fill=seq_folder)) +
geom_boxplot() +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
theme(plot.title = element_text(hjust=0.5, face="bold")) +
ggtitle("NCells vs NGenes")
ngenes
FigX: fig legend
# transcritps
dtranscripts <- ggplot(metadata, aes(x=log10GenesPerUMI, color = seq_folder, fill=seq_folder)) +
geom_density(alpha = 0.2) +
theme_classic() +
geom_vline(xintercept = 0.8)
dtranscripts
FigX: fig legend
# mito ratiomtratio <- ggplot(metadata, aes(color=seq_folder, x=mitoRatio, fill=seq_folder)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
geom_vline(xintercept = 0.2)
mtratio
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Warning: Removed 21 rows containing non-finite values (stat_density).
FigX: fig legend
bapd8.combined <- bapd8.temp
df <- bapd8.combined@meta.data
df$replicate <- NA
df$replicate[which(str_detect(df$orig.ident, "5pcO2"))] <- "5pcO2"
df$replicate[which(str_detect(df$orig.ident, "20pcO2"))] <- "20pcO2"
df$replicate[which(str_detect(df$orig.ident, "nCT_"))] <- "nCT"
df$replicate[which(str_detect(df$orig.ident, "nTE_"))] <- "nTE"
bapd8.combined@meta.data <- df
bapd8.combined[["percent.mt"]] <- PercentageFeatureSet(bapd8.combined, pattern = "^MT-")p <- VlnPlot(bapd8.combined, features = "nFeature_RNA", pt.size = 1) +
geom_hline(yintercept=200, color = "red", size=1) +
geom_hline(yintercept=10000, color = "red", size=1) +
theme(legend.position = "none")
q <- VlnPlot(bapd8.combined, features = "nCount_RNA", pt.size = 1) +
theme(legend.position = "none")
r <- VlnPlot(bapd8.combined, features = "percent.mt", pt.size = 1) +
geom_hline(yintercept=15, color = "red", size=1) +
theme(legend.position = "none")
panel_plot <- plot_grid(p, q, r, labels=c("A", "B", "C"), ncol=3, nrow = 1)
panel_plot
FigX: fig legend
B1 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "percent.mt")
B2 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
bapd8.combined <- subset(bapd8.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 10000 & percent.mt < 25)
I1 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "percent.mt")
I2 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
bapd8.combined <- subset(bapd8.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 10000 & percent.mt < 15)
A1 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "percent.mt")
A2 <- FeatureScatter(bapd8.combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
B <- B1 | B2
I <- I1 | I2
A <- A1 | A2
panel_plot <- plot_grid(B1, B2, I1, I2, A1, A2, labels=c("A", "B", "C", "D", "E", "F"), ncol=2, nrow = 3)
panel_plot
FigX: fig legend
counts <- GetAssayData(object = bapd8.combined, slot = "counts")
counts <- counts[grep(pattern = "^MT", x = rownames(counts), invert = TRUE),]
counts <- counts[grep(pattern = "^MT", x = rownames(counts), invert = TRUE),]
counts <- counts[grep(pattern = "^RPL", x = rownames(counts), invert = TRUE),]
counts <- counts[grep(pattern = "^RPS", x = rownames(counts), invert = TRUE),]
counts <- counts[grep(pattern = "^MRPS", x = rownames(counts), invert = TRUE),]
counts <- counts[grep(pattern = "^MRPL", x = rownames(counts), invert = TRUE),]
keep_genes <- Matrix::rowSums(counts) >= 10
filtered_counts <- counts[keep_genes, ]
bapd8.fcombined <- CreateSeuratObject(filtered_counts, meta.data = bapd8.combined@meta.data)
bapd8.fcombined@meta.data <- bapd8.fcombined@meta.data[1:4]
bapd8.combined <- bapd8.fcombinedbapd8.list <- SplitObject(bapd8.combined, split.by = "orig.ident")
bapd8.list <- lapply(X = bapd8.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
bapd8.anchors <- FindIntegrationAnchors(object.list = bapd8.list, dims = 1:20)
bapd8.integrated <- IntegrateData(anchorset = bapd8.anchors, dims = 1:20)
DefaultAssay(bapd8.integrated) <- "integrated"
bapd8.integrated <- ScaleData(bapd8.integrated, verbose = FALSE)
bapd8.integrated <- RunPCA(bapd8.integrated, npcs = 30, verbose = FALSE)
bapd8.integrated <- RunUMAP(bapd8.integrated, reduction = "pca", dims = 1:20)
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session
bapd8.integrated <- FindNeighbors(bapd8.integrated, reduction = "pca", dims = 1:20)
bapd8.integrated <- FindClusters(bapd8.integrated, resolution = 0.5)
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 17208
#> Number of edges: 610547
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8716
#> Number of communities: 13
#> Elapsed time: 1 seconds
num.clusters <- nlevels(bapd8.integrated$seurat_clusters)
num.clusters
#> [1] 13VizDimLoadings(bapd8.integrated, dims = 1:4, reduction = "pca")
FigX: fig legend
bapd8.integrated <- JackStraw(bapd8.integrated, num.replicate = 100)
bapd8.integrated <- ScoreJackStraw(bapd8.integrated, dims = 1:20)
elbow <- ElbowPlot(bapd8.integrated)
jack <- JackStrawPlot(bapd8.integrated, dims = 1:20)
panel_plot <- plot_grid(elbow, jack, labels = c('A', 'B'), ncol = 2)
#> Warning: Removed 28000 rows containing missing values (geom_point).
panel_plot
FigX: fig legend
df <- bapd8.integrated@meta.data
df$new_clusters <- as.factor(as.numeric(df$seurat_clusters))
bapd8.integrated@meta.data <- df
Idents(bapd8.integrated) <- "new_clusters"A = enhancedDimPlot(object = bapd8.integrated,
grouping_var = 'ident',
reduction = "umap",
label = TRUE,
pt.size = 1,
alpha = 0.5) +
ggtitle("A") +
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_classic() +
theme(legend.position = "none",
plot.title = element_text(face = "bold"))
B <- enhancedDimPlot(object = bapd8.integrated,
grouping_var = 'replicate',
reduction = "umap",
label = FALSE,
pt.size = 1,
alpha = 0.4) +
ggtitle("B") +
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_classic() +
theme(legend.justification = c(1, 1),
legend.position = c(1, 1),
plot.title = element_text(face = "bold")) +
scale_colour_manual(name = "Conditions",
labels = c(expression(paste('20% ', 'O'[2])),
expression(paste('5% ', 'O'[2])),
'nCT',
'nTE'),
values = c("20pcO2" = "#DA3C96",
"5pcO2" = "#A90065",
"nCT" = "#FFD74D",
"nTE" = "#9BC13C" )) +
scale_fill_manual(name = "Conditions",
labels = c(expression(paste('20% ', 'O'[2])),
expression(paste('5% ', 'O'[2])),
'nCT',
'nTE'),
values = c("20pcO2" = "#DA3C96",
"5pcO2" = "#A90065",
"nCT" = "#FFD74D",
"nTE" = "#9BC13C")) +
scale_linetype_manual(values = "blank")
C <- enhancedDimPlot(object = bapd8.integrated,
grouping_var = 'orig.ident',
reduction = "umap",
label = FALSE,
pt.size = 1,
alpha = 0.4) +
ggtitle("C") +
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_classic() +
theme(legend.justification = c(1, 1),
legend.position = c(1, 1),
plot.title = element_text(face = "bold")) +
scale_colour_manual(name = "Replicates",
labels = c(expression(paste('20% ', 'O'[2], ' rep1')),
expression(paste('20% ', 'O'[2], ' rep2')),
expression(paste('5% ', 'O'[2], ' rep1')),
expression(paste('5% ', 'O'[2], ' rep1')),
"nCT day 5",
"nCT day 10",
"nTE day 3",
"nTE day 5"),
values = c("20pcO2_r1" = "#0571b0",
"20pcO2_r2" = "#92c5de",
"5pcO2_r1" = "#ca0020",
"5pcO2_r2" = "#f4a582",
"nCT_D5" = "#d133ff",
"nCT_D10" = "#ff33f6",
"nTE_D2" = "#33ffa2",
"nTE_D3" = "#5bff33")) +
scale_fill_manual(name = "Replicates",
labels = c(expression(paste('20% ', 'O'[2], ' rep1')),
expression(paste('20% ', 'O'[2], ' rep2')),
expression(paste('5% ', 'O'[2], ' rep1')),
expression(paste('5% ', 'O'[2], ' rep1')),
"nCT day 5",
"nCT day 10",
"nTE day 3",
"nTE day 5"),
values = c("20pcO2_r1" = "#0571b0",
"20pcO2_r2" = "#92c5de",
"5pcO2_r1" = "#ca0020",
"5pcO2_r2" = "#f4a582",
"nCT_D5" = "#d133ff",
"nCT_D10" = "#ff33f6",
"nTE_D2" = "#33ffa2",
"nTE_D3" = "#5bff33")) +
scale_linetype_manual(values = "blank")
panel_plot <- plot_grid(A, B, ncol=2, nrow = 1)
panel_plot
FigX: fig legend
DimPlot(object = bapd8.integrated,
split.by = 'orig.ident', ncol = 4) +
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_classic() +
theme(legend.position = "none")
FigX: fig legend
A = enhancedDimPlot(object = bapd8.integrated,
grouping_var = 'ident',
reduction = "umap",
label = FALSE,
pt.size = 1,
alpha = 0.5) +
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_classic() +
theme(legend.position = "none")
ggplotly(A)FigX: fig legend
DefaultAssay(bapd8.integrated) <- "RNA"
for (i in 1:num.clusters){
try({
cluster.markers.all <- FindMarkers(bapd8.integrated, ident.1 = i)
cluster.markers.filtered <- cluster.markers.all %>% filter(avg_log2FC >= 0.584962501) %>% filter(p_val_adj <= 0.05) %>% arrange(desc(avg_log2FC))
markers.filtered.names <- rownames(cluster.markers.filtered)
assign( paste("cluster.marker.names", i, sep = "."), markers.filtered.names )
})
}fullCounts <- tibble(
cluster = bapd8.integrated@meta.data$new_clusters,
cell_type = bapd8.integrated@meta.data$orig.ident) %>%
dplyr::group_by(cluster, cell_type) %>%
dplyr::count() %>%
dplyr::group_by(cluster) %>%
dplyr::ungroup() %>%
dplyr::mutate(cluster=paste0("Cluster_",cluster))
fullCounts <- fullCounts %>%
group_by(cell_type) %>%
mutate(cell_type_sum = sum(n)) %>%
mutate(percent=(n*100)/cell_type_sum)
list2env(split(fullCounts, fullCounts$cluster), envir = .GlobalEnv)
#> <environment: R_GlobalEnv>mybarplot <- function(pdata, i) {
ggplot(data=pdata, aes(x=cell_type, y=percent, fill = cell_type, alpha = 0.5)) +
geom_bar(stat="identity") +
theme_classic(base_size = 12) +
theme(legend.position="none",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
ggtitle(paste("Cluster", i, "cell composition")) + ylab("% cells in cluster") + xlab("") +
scale_colour_manual(name = "Replicates",
labels = c(expression(paste('20% ', 'O'[2], ' rep1')),
expression(paste('20% ', 'O'[2], ' rep2')),
expression(paste('5% ', 'O'[2], ' rep1')),
expression(paste('5% ', 'O'[2], ' rep1')),
"nCT day 5",
"nCT day 10",
"nTE day 3",
"nTE day 5"),
values = c("20pcO2_r1" = "#DA3C96",
"20pcO2_r2" = "#DA3C96",
"5pcO2_r1" = "#A90065",
"5pcO2_r2" = "#A90065",
"nCT_D5" = "#FFD74D",
"nCT_D10" = "#FFD74D",
"nTE_D2" = "#9BC13C",
"nTE_D3" = "#9BC13C")) +
scale_fill_manual(name = "Replicates",
labels = c(expression(paste('20% ', 'O'[2], ' rep1')),
expression(paste('20% ', 'O'[2], ' rep2')),
expression(paste('5% ', 'O'[2], ' rep1')),
expression(paste('5% ', 'O'[2], ' rep1')),
"nCT day 5",
"nCT day 10",
"nTE day 3",
"nTE day 5"),
values = c("20pcO2_r1" = "#DA3C96",
"20pcO2_r2" = "#DA3C96",
"5pcO2_r1" = "#A90065",
"5pcO2_r2" = "#A90065",
"nCT_D5" = "#FFD74D",
"nCT_D10" = "#FFD74D",
"nTE_D2" = "#9BC13C",
"nTE_D3" = "#9BC13C")) +
scale_linetype_manual(values = "blank")
}# colors for each clusters defined
ggplotColours <- function(n = 6, h = c(0, 360) + 15){
if ((diff(h) %% 360) < 1) h[2] <- h[2] - 360/n
hcl(h = (seq(h[1], h[2], length = n)), c = 100, l = 65)
}
color_list <- ggplotColours(n=13)grouped_violinPlots <- function(markersfile, clusternumber, seuratobject = bapd8.integrated) {
dittoPlotVarsAcrossGroups(seuratobject, markersfile,
group.by = "new_clusters", main = paste("Cluster ", clusternumber, " markers expression"),
xlab = "",
ylab = "Mean z-score expression",
x.labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4",
"Cluster 5", "Cluster 6", "Cluster 7", "Cluster 8",
"Cluster 9", "Cluster 10", "Cluster 11", "Cluster 12",
"Cluster 13"),
vlnplot.lineweight = 0.5,
legend.show = FALSE,
jitter.size = 0.5,
color.panel = color_list)
}l <- load(file = "~/TutejaLab/PlacentaEnrich/combine-test-expression1.Rdata")
humanGeneMapping <- dataset$GRCH38$humanGeneMapping
d <- dataset$PlacentaDeciduaBloodData
data <- d$expressionData
cellDetails <- d$cellDetailsrunpce <- function(inputgenelist, clusternumber, shade) {
inputGenes<-toupper(inputgenelist)
humanGene<-humanGeneMapping[humanGeneMapping$Gene.name %in% inputGenes,]
inputGenes<-humanGene$Gene
expressionData<-data[intersect(row.names(data),humanGeneMapping$Gene),]
se<-SummarizedExperiment(assays = SimpleList(as.matrix(expressionData)),rowData = row.names(expressionData),colData = colnames(expressionData))
cellSpecificGenesExp<-teGeneRetrieval(se,expressedGeneThreshold = 1)
print(length(inputGenes))
gs<-GeneSet(geneIds=toupper(inputGenes))
output2<-teEnrichmentCustom(gs,cellSpecificGenesExp)
enrichmentOutput<-setNames(data.frame(assay(output2[[1]]),row.names = rowData(output2[[1]])[,1]),colData(output2[[1]])[,1])
row.names(cellDetails)<-cellDetails$RName
enrichmentOutput$Tissue<- cellDetails[row.names(enrichmentOutput),"CellName"]
ggplot(data = enrichmentOutput, mapping = aes(x = reorder (Tissue, -Log10PValue), Log10PValue)) +
geom_bar(stat = "identity", color = shade, fill = shade) + theme_classic(base_size = 10) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 10), plot.margin = unit(c(1,1,1,3), "cm")) +
ggtitle(paste0("Cluster ", clusternumber, " PCE")) +
ylab("-log10 p-value (adj.)") +
xlab("") +
scale_y_continuous(expand = expansion(mult = c(0, .1)))
}pce <- runpce(cluster.marker.names.1, 1, color_list[1])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_1, 1)
violin <- grouped_violinPlots(cluster.marker.names.1, 1)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.2, 2, color_list[2])
#> [1] 44
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_2, 2)
violin <- grouped_violinPlots(cluster.marker.names.2, 2)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.3, 3, color_list[3])
#> [1] 244
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_3, 3)
violin <- grouped_violinPlots(cluster.marker.names.3, 3)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.4, 4, color_list[4])
#> [1] 210
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_4, 4)
violin <- grouped_violinPlots(cluster.marker.names.4, 4)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.5, 5, color_list[5])
#> [1] 32
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_5, 5)
violin <- grouped_violinPlots(cluster.marker.names.5, 5)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.6, 6, color_list[6])
#> [1] 355
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_6, 6)
violin <- grouped_violinPlots(cluster.marker.names.6, 6)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.7, 7, color_list[7])
#> [1] 38
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_7, 7)
violin <- grouped_violinPlots(cluster.marker.names.7, 7)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 8, color_list[8])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_8, 8)
violin <- grouped_violinPlots(cluster.marker.names.8, 8)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 9, color_list[9])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_9, 9)
violin <- grouped_violinPlots(cluster.marker.names.9, 9)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 10, color_list[10])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_10, 10)
violin <- grouped_violinPlots(cluster.marker.names.10, 10)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 11, color_list[11])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_11, 11)
violin <- grouped_violinPlots(cluster.marker.names.11, 11)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 12, color_list[12])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_12, 12)
violin <- grouped_violinPlots(cluster.marker.names.12, 12)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
pce <- runpce(cluster.marker.names.1, 13, color_list[13])
#> [1] 68
#> No background list provided. Using all the
#> genes as background.
count <- mybarplot(Cluster_13, 13)
violin <- grouped_violinPlots(cluster.marker.names.13, 13)
toprow <- plot_grid(count, violin, labels = c('A', 'B'), align = 'h')
panel_plot <- plot_grid(toprow, pce, labels = c('', 'C'), ncol = 1, rel_heights = c(1, 1.3))
panel_plot
FigX: fig legend
sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19042)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] grid stats4 parallel stats graphics grDevices utils
#> [8] datasets methods base
#>
#> other attached packages:
#> [1] plotly_4.9.3 scales_1.1.1
#> [3] ComplexHeatmap_2.6.2 dittoSeq_1.2.6
#> [5] ggrepel_0.9.1 calibrate_1.7.7
#> [7] MASS_7.3-54 enhancedDimPlot_0.0.0.9100
#> [9] forcats_0.5.1 purrr_0.3.4
#> [11] readr_1.4.0 tidyr_1.1.3
#> [13] tibble_3.1.1 tidyverse_1.3.1
#> [15] gprofiler2_0.2.0 TissueEnrich_1.10.1
#> [17] GSEABase_1.52.1 graph_1.68.0
#> [19] annotate_1.68.0 XML_3.99-0.6
#> [21] AnnotationDbi_1.52.0 SummarizedExperiment_1.20.0
#> [23] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
#> [25] IRanges_2.24.1 S4Vectors_0.28.1
#> [27] MatrixGenerics_1.2.1 matrixStats_0.58.0
#> [29] ensurer_1.1 stringr_1.4.0
#> [31] dplyr_1.0.7 gridExtra_2.3
#> [33] multtest_2.46.0 Biobase_2.50.0
#> [35] BiocGenerics_0.36.1 metap_1.4
#> [37] patchwork_1.1.1 cowplot_1.1.1
#> [39] ggplot2_3.3.5 kableExtra_1.3.4
#> [41] knitr_1.33 SeuratWrappers_0.3.0
#> [43] SeuratObject_4.0.1 Seurat_4.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] scattermore_0.7 bit64_4.0.5
#> [3] irlba_2.3.3 multcomp_1.4-17
#> [5] DelayedArray_0.16.3 data.table_1.14.0
#> [7] rpart_4.1-15 RCurl_1.98-1.3
#> [9] generics_0.1.0 TH.data_1.0-10
#> [11] RSQLite_2.2.7 RANN_2.6.1
#> [13] future_1.21.0 bit_4.0.4
#> [15] mutoss_0.1-12 spatstat.data_2.1-0
#> [17] webshot_0.5.2 xml2_1.3.2
#> [19] lubridate_1.7.10 httpuv_1.6.1
#> [21] assertthat_0.2.1 xfun_0.22
#> [23] hms_1.1.0 jquerylib_0.1.4
#> [25] evaluate_0.14 promises_1.2.0.1
#> [27] fansi_0.4.2 dbplyr_2.1.1
#> [29] readxl_1.3.1 igraph_1.2.6
#> [31] DBI_1.1.1 tmvnsim_1.0-2
#> [33] htmlwidgets_1.5.3 spatstat.geom_2.1-0
#> [35] paletteer_1.3.0 ellipsis_0.3.2
#> [37] crosstalk_1.1.1 RSpectra_0.16-0
#> [39] backports_1.2.1 bookdown_0.22
#> [41] deldir_0.2-10 vctrs_0.3.8
#> [43] SingleCellExperiment_1.12.0 remotes_2.3.0
#> [45] Cairo_1.5-12.2 ROCR_1.0-11
#> [47] abind_1.4-5 cachem_1.0.5
#> [49] withr_2.4.2 sctransform_0.3.2
#> [51] goftest_1.2-2 mnormt_2.0.2
#> [53] svglite_2.0.0 cluster_2.1.2
#> [55] lazyeval_0.2.2 crayon_1.4.1
#> [57] labeling_0.4.2 edgeR_3.32.1
#> [59] pkgconfig_2.0.3 nlme_3.1-152
#> [61] rlang_0.4.11 globals_0.14.0
#> [63] lifecycle_1.0.0 miniUI_0.1.1.1
#> [65] sandwich_3.0-1 mathjaxr_1.4-0
#> [67] modelr_0.1.8 rsvd_1.0.5
#> [69] cellranger_1.1.0 polyclip_1.10-0
#> [71] lmtest_0.9-38 Matrix_1.3-3
#> [73] zoo_1.8-9 reprex_2.0.0
#> [75] ggridges_0.5.3 GlobalOptions_0.1.2
#> [77] pheatmap_1.0.12 png_0.1-7
#> [79] viridisLite_0.4.0 rjson_0.2.20
#> [81] bitops_1.0-7 KernSmooth_2.23-20
#> [83] blob_1.2.1 shape_1.4.6
#> [85] parallelly_1.25.0 memoise_2.0.0
#> [87] magrittr_2.0.1 plyr_1.8.6
#> [89] ica_1.0-2 zlibbioc_1.36.0
#> [91] compiler_4.0.5 RColorBrewer_1.1-2
#> [93] clue_0.3-59 plotrix_3.8-1
#> [95] fitdistrplus_1.1-5 cli_2.5.0
#> [97] XVector_0.30.0 listenv_0.8.0
#> [99] pbapply_1.4-3 mgcv_1.8-35
#> [101] tidyselect_1.1.1 stringi_1.6.2
#> [103] highr_0.9 yaml_2.2.1
#> [105] locfit_1.5-9.4 sass_0.4.0
#> [107] tools_4.0.5 future.apply_1.7.0
#> [109] circlize_0.4.12 rstudioapi_0.13
#> [111] farver_2.1.0 rmdformats_1.0.3
#> [113] Rtsne_0.15 digest_0.6.27
#> [115] BiocManager_1.30.15 shiny_1.6.0
#> [117] Rcpp_1.0.6 broom_0.7.6
#> [119] later_1.2.0 RcppAnnoy_0.0.18
#> [121] httr_1.4.2 Rdpack_2.1.1
#> [123] colorspace_2.0-1 rvest_1.0.0
#> [125] fs_1.5.0 tensor_1.5
#> [127] reticulate_1.20 splines_4.0.5
#> [129] uwot_0.1.10 rematch2_2.1.2
#> [131] sn_2.0.0 spatstat.utils_2.1-0
#> [133] systemfonts_1.0.2 xtable_1.8-4
#> [135] jsonlite_1.7.2 R6_2.5.0
#> [137] TFisher_0.2.0 pillar_1.6.1
#> [139] htmltools_0.5.1.1 mime_0.10
#> [141] glue_1.4.2 fastmap_1.1.0
#> [143] codetools_0.2-18 mvtnorm_1.1-1
#> [145] utf8_1.2.1 lattice_0.20-44
#> [147] bslib_0.2.5.1 spatstat.sparse_2.0-0
#> [149] numDeriv_2016.8-1.1 leiden_0.3.8
#> [151] survival_3.2-11 limma_3.46.0
#> [153] rmarkdown_2.10.1 munsell_0.5.0
#> [155] GetoptLong_1.0.5 GenomeInfoDbData_1.2.4
#> [157] haven_2.4.1 reshape2_1.4.4
#> [159] gtable_0.3.0 rbibutils_2.1.1
#> [161] spatstat.core_2.1-2